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政大典藏 > College of Informatics > Department of Computer Science > Periodical Articles > Item 140.119/69150

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/69150

Title:	A Graph-Based Approach for Discovering Various Types of Association Rules
Authors:	陳良弼 Yen,Show-Jane;Chen,Arbee L.P.
Contributors:	資科系
Keywords:	Data mining;knowledge discovery;association rule;association pattern;graph-based approach
Date:	2001-09
Issue Date:	2014-08-21 15:08:51 (UTC+8)
Abstract:	Mining association rules is an important task for knowledge discovery. We can analyze past transaction data to discover customer behaviors such that the quality of business decision can be improved. Various types of association rules may exist in a large database of customer transactions. The strategy of mining association rules focuses on discovering large itemsets, which are groups of items which appear together in a sufficient number of transactions. In this paper, we propose a graph-based approach to generate various types of association rules from a large database of customer transactions. This approach scans the database once to construct an association graph and then traverses the graph to generate all large itemsets. Empirical evaluations show that our algorithms outperform other algorithms which need to make multiple passes over the database.
Relation:	IEEE Transactions on Knowledge and Data Engineering (EI,SCI),13(5),839-845
Data Type:	article
Appears in Collections:	[Department of Computer Science ] Periodical Articles

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